OpenRaiser/Intern-Atlas huggingface dataset:
SOA Governance - Road into Maturity
Venue: arXiv cs.CY 2016
Year: 2016

There is a general consensus that SOA benefits could be reached but it is unclear how to achieve this. Research shows that the problems with SOA governance in practice are among the major reasons of SOA failures. Based on a literature review, this study first proposes a list of SOA aspects to be considered when implementing SOA governance. By adopting an interpretive research methodology based on interviews, this research paper makes two contributions: it addresses the practical matters that are major concerns for organisations to achieve a higher maturity level with their SOA, and it reveals the importance of the key SOA aspects in building strong governance and consequently reaching a higher maturity level. The expected result should deliver a theoretical contribution to SOA maturity in relation to SOA governance; it could provide organisations with new awareness in assessing their level of maturity and provide recommendations.
Improving Recurrent Neural Networks For Sequence Labelling
Venue: arXiv cs.CL 2016
Year: 2016

In this paper we study different types of Recurrent Neural Networks (RNN) for sequence labeling tasks. We propose two new variants of RNNs integrating improvements for sequence labeling, and we compare them to the more traditional Elman and Jordan RNNs. We compare all models, either traditional or new, on four distinct tasks of sequence labeling: two on Spoken Language Understanding (ATIS and MEDIA); and two of POS tagging for the French Treebank (FTB) and the Penn Treebank (PTB) corpora. The results show that our new variants of RNNs are always more effective than the others.
Self-service Ad-hoc Querying Using Controlled Natural Language
Venue: arXiv cs.DB 2016
Year: 2016

The ad-hoc querying process is slow and error prone due to inability of business experts of accessing data directly without involving IT experts. The problem lies in complexity of means used to query data. We propose a new natural language- and semistar ontology-based ad-hoc querying approach which lowers the steep learning curve required to be able to query data. The proposed approach would significantly shorten the time needed to master the ad-hoc querying and to gain the direct access to data by business experts, thus facilitating the decision making process in enterprises, government institutions and other organizations.
Robot-stated limitations but not intentions promote user assistance
Venue: arXiv cs.RO 2016
Year: 2016

Human-Robot-Interaction (HRI) research is typically built around the premise that the robot serves to assist a human in achieving a human-led goal or shared task. However, there are many circumstances during HRI in which a robot may need the assistance of a human in shared tasks or to achieve goals. We use the ROBO-GUIDE model as a case study, and insights from social psychology, to examine how a robot's personality can impact on user cooperation. A study of 364 participants indicates that individuals may prefer to use likable social robots ahead of those designed to appear more capable; this outcome reflects known social decisions in human interpersonal relationships. This work further demonstrates the value of social psychology in developing social robots and exploring HRI.
Fast and Extensible Online Multivariate Kernel Density Estimation
Venue: arXiv cs.LG 2016
Year: 2016

We present xokde++, a state-of-the-art online kernel density estimation approach that maintains Gaussian mixture models input data streams. The approach follows state-of-the-art work on online density estimation, but was redesigned with computational efficiency, numerical robustness, and extensibility in mind. Our approach produces comparable or better results than the current state-of-the-art, while achieving significant computational performance gains and improved numerical stability. The use of diagonal covariance Gaussian kernels, which further improve performance and stability, at a small loss of modelling quality, is also explored. Our approach is up to 40 times faster, while requiring 90\% less memory than the closest state-of-the-art counterpart.
Formal Semantics and Soundness of a Translation from Event-B Actions to SQL Statements
Venue: arXiv cs.SE 2016
Year: 2016

The EventB2SQL tool translates Event-B models to persistent Java applications that store the state of the model in a relational database. Most Event-B assignments are translated directly to SQL database modification statements, which can then be executed against the database. In this work, we present a formal semantics for and prove the soundness of the translation of sets of assignment statements representing the actions of an Event-B event. This allows the generated code to be used with confidence in its correctness.
Distance, Borders, and Time: The Diffusion and Permeability of Political Violence in North and West Africa
Venue: arXiv cs.SI 2016
Year: 2016

This paper explores the spatial and temporal diffusion of political violence in North and West Africa. It does so by endeavoring to represent the mental landscape that lives in the back of a group leader's mind as he contemplates strategic targeting. We assume that this representation is a combination of the physical geography of the target environment, and the mental and physical cost of following a seemingly random pattern of attacks. Focusing on the distance and time between attacks and taking into consideration the transaction costs that state boundaries impose, we wish to understand what constrains a group leader to attack at a location other than the one that would seem to yield the greatest overt payoff. By its very nature, the research problem defies the collection of a full set of structural data. Instead, we leverage functional data from the Armed Conflict Location and Event Data project (ACLED) dataset that, inter alia, meticulously catalogues violent extremist incidents in North and West Africa since 1997, to generate a network whose nodes are administrative regions. These nodes are connected by edges of qualitatively different types: undirected edges representing geographic distance, undirected edges representing borders, and directed edges representing consecutive attacks by the same group at the two endpoints. We analyze the resulting network using novel spectral embedding techniques that are able to account fully for the different types of edges. The result is a map of North and West Africa that depicts the permeability to violence. A better understanding of how location, time, and borders condition attacks enables planning, prepositioning, and response.
ChinMotion Rapidly Enables 3D Computer Interaction after Tetraplegia
Venue: arXiv cs.HC 2016
Year: 2016

Individuals with severe paralysis require hands-free interfaces to control assistive devices that can improve their quality of life. We present ChinMotion, an interface that noninvasively harnesses preserved chin, lip and tongue sensorimotor function after tetraplegia to convey intuitive control commands. After two hours of practice, ChinMotion enables superior point-and-click performance over existing interfaces and it facilitates accurate 3D control of a virtual robotic arm.
On Uplink User Capacity for Massive MIMO Cellular Networks
Venue: arXiv cs.IT 2016
Year: 2016

Under the conditions where performance in a massive MIMO network is limited by pilot contamination, the reverse link signal-to-interference ratio (SIR) exhibits different distributions when using different pilot allocation schemes. By utilising different sets of orthogonal pilot sequences, as opposed to reused sequences amongst adjacent cells, the resulting SIR distribution is more favourable with respect to maximising the number of users on the network while maintaining a given quality of service (QoS) for all users. This paper provides a simple expression for uplink user capacity on such networks and presents uplink user capacity figures for both pilot allocation schemes for a selection of quality of service targets.
Machine Learning Techniques and Applications For Ground-based Image Analysis
Venue: arXiv cs.CV 2016
Year: 2016

Ground-based whole sky cameras have opened up new opportunities for monitoring the earth's atmosphere. These cameras are an important complement to satellite images by providing geoscientists with cheaper, faster, and more localized data. The images captured by whole sky imagers can have high spatial and temporal resolution, which is an important pre-requisite for applications such as solar energy modeling, cloud attenuation analysis, local weather prediction, etc. Extracting valuable information from the huge amount of image data by detecting and analyzing the various entities in these images is challenging. However, powerful machine learning techniques have become available to aid with the image analysis. This article provides a detailed walk-through of recent developments in these techniques and their applications in ground-based imaging. We aim to bridge the gap between computer vision and remote sensing with the help of illustrative examples. We demonstrate the advantages of using machine learning techniques in ground-based image analysis via three primary applications -- segmentation, classification, and denoising.
Light fidelity (LI-FI) based indoor communication system
Venue: arXiv cs.NI 2016
Year: 2016

Indoor wireless communication is an essential part of next generation wireless communication system.For an indoor communication number of users and their device are increasing very rapidly so as a result capacity of frequency spectrum to accommodate further users in future is limited and also it would be difficult for service providers to provide more user reliable and high speed communication so this short come can be solve in future by using Li-Fi based indoor communication system. Li-Fi which is an emerging branch of optical wireless communication can be useful in future as a replacement and backup of Wireless Fidelity (Wi-Fi)for indoor communication because it can provide high data rate of transmission along with high capacity to utilize more users as its spectrum bandwidth is much broader than the radio spectrum. In this paper we will look at the different aspects of the Li-Fi based indoor communication system,summarizes some of the research conducted so far and we will also proposed a Li-Fi based communication model keeping in mind coverage area for multiple user and evaluate its performance under different scenarios .
Inverse Mellin Transform of Holonomic Sequences
Venue: arXiv cs.SC 2016
Year: 2016

We describe a method to compute the inverse Mellin transform of holonomic sequences, that is based on a method to compute the Mellin transform of holonomic functions. Both methods are implemented in the computer algebra package HarmonicSums.
Cache-enabled Device-to-Device Communications: Offloading Gain and Energy Cost
Venue: arXiv cs.IT 2016
Year: 2016

By caching files at users, content delivery traffic can be offloaded via device-to-device (D2D) links if a helper user is willing to transmit the cached file to the user who requests the file. In practice, the user device has limited battery capacity, and may terminate the D2D connection when its battery has little energy left. Thus, taking the battery consumption allowed by the helper users to support D2D into account introduces a reduction in the possible amount of offloading. In this paper, we investigate the relationship between offloading gain of the system and energy cost of each helper user. To this end, we introduce a user-centric protocol to control the energy cost for a helper user to transmit the file. Then, we optimize the proactive caching policy to maximize the offloading opportunity, and optimize the transmit power at each helper to maximize the offloading probability. Finally, we evaluate the overall amount of traffic offloaded to D2D links and evaluate the average energy consumption at each helper, with the optimized caching policy and transmit power. Simulations show that a significant amount of traffic can be offloaded even when the energy cost is kept low.
Linguistic Input Features Improve Neural Machine Translation
Venue: arXiv cs.CL 2016
Year: 2016

Neural machine translation has recently achieved impressive results, while using little in the way of external linguistic information. In this paper we show that the strong learning capability of neural MT models does not make linguistic features redundant; they can be easily incorporated to provide further improvements in performance. We generalize the embedding layer of the encoder in the attentional encoder--decoder architecture to support the inclusion of arbitrary features, in addition to the baseline word feature. We add morphological features, part-of-speech tags, and syntactic dependency labels as input features to English<->German, and English->Romanian neural machine translation systems. In experiments on WMT16 training and test sets, we find that linguistic input features improve model quality according to three metrics: perplexity, BLEU and CHRF3. An open-source implementation of our neural MT system is available, as are sample files and configurations.
A strategy to implement Dirichlet boundary conditions in the context of ADER finite volume schemes. One-dimensional conservation laws
Venue: arXiv cs.NA 2016
Year: 2016

ADER schemes are numerical methods, which can reach an arbitrary order of accuracy in both space and time. They are based on a reconstruction procedure and the solution of generalized Riemann problems. However, for general boundary conditions, in particular of Dirichlet type, a lack of accuracy might occur if a suitable treatment of boundaries conditions is not properly carried out. In this work the treatment of Dirichlet boundary conditions for conservation laws in the context of ADER schemes, is concerned. The solution of generalized Riemann problems at the extremes of the computational domain, provides the correct influence of boundaries. The reconstruction procedure, for data near to the boundaries, demands for information outside the computational domain, which is carried out in terms of ghost cells, which are provided by using the numerical solution of auxiliary problems. These auxiliary problems are hyperbolic and they are constructed from the conservation laws and the information at boundaries, which may be partially or totally known in terms of prescribed functions. The evolution of these problems, unlike to the usual manner, is done in space rather than in time due to that these problems are named here, {\it reverse problems}. The methodology can be considered as a numerical counterpart of the inverse Lax-Wendroff procedure for filling ghost cells. However, the use of Taylor series expansions, as well as, Lax-Wendroff procedure, are avoided. For the scalar case is shown that the present procedure preserve the accuracy of the scheme which is reinforced with some numerical results. Expected orders of accuracy for solving conservation laws by using the proposed strategy at boundaries, are obtained up to fifth-order in both space and time.
The "Horse'' Inside: Seeking Causes Behind the Behaviours of Music Content Analysis Systems
Venue: arXiv cs.SD 2016
Year: 2016

Building systems that possess the sensitivity and intelligence to identify and describe high-level attributes in music audio signals continues to be an elusive goal, but one that surely has broad and deep implications for a wide variety of applications. Hundreds of papers have so far been published toward this goal, and great progress appears to have been made. Some systems produce remarkable accuracies at recognising high-level semantic concepts, such as music style, genre and mood. However, it might be that these numbers do not mean what they seem. In this paper, we take a state-of-the-art music content analysis system and investigate what causes it to achieve exceptionally high performance in a benchmark music audio dataset. We dissect the system to understand its operation, determine its sensitivities and limitations, and predict the kinds of knowledge it could and could not possess about music. We perform a series of experiments to illuminate what the system has actually learned to do, and to what extent it is performing the intended music listening task. Our results demonstrate how the initial manifestation of music intelligence in this state-of-the-art can be deceptive. Our work provides constructive directions toward developing music content analysis systems that can address the music information and creation needs of real-world users.
Optimizing quantization for Lasso recovery
Venue: arXiv cs.IT 2016
Year: 2016

This letter is focused on quantized Compressed Sensing, assuming that Lasso is used for signal estimation. Leveraging recent work, we provide a framework to optimize the quantization function and show that the recovered signal converges to the actual signal at a quadratic rate as a function of the quantization level. We show that when the number of observations is high, this method of quantization gives a significantly better recovery rate than standard Lloyd-Max quantization. We support our theoretical analysis with numerical simulations.
Sentence Similarity Measures for Fine-Grained Estimation of Topical Relevance in Learner Essays
Venue: arXiv cs.CL 2016
Year: 2016

We investigate the task of assessing sentence-level prompt relevance in learner essays. Various systems using word overlap, neural embeddings and neural compositional models are evaluated on two datasets of learner writing. We propose a new method for sentence-level similarity calculation, which learns to adjust the weights of pre-trained word embeddings for a specific task, achieving substantially higher accuracy compared to other relevant baselines.
Cyber Attack Thread: A Control-flow Based Approach to Deconstruct and Mitigate Cyber Threats
Venue: arXiv cs.NI 2016
Year: 2016

Attacks in cyberspace have got attention due to risk at privacy, breach of trust and financial losses for individuals as well as organizations. In recent years, these attacks have become more complex to analyze technically, as well as to detect and prevent from accessing confidential data. Although there are many methodologies and mechanisms which have been suggested for cyber-attack detection and prevention, but not from the perspective of an attacker. This paper presents the cyber-defence as hindrances, faced by the attacker, by understanding attack thread and defence possibilities with existing security mechanisms. Seven phases of Cyber Attack Thread are introduced and technical aspects are discussed with reference to APT attacks. The paper aims for security practitioner and administrators as well as for the general audience to understand the attack scenario and defensive security measures.
Technical Aspects of Cyber Kill Chain
Venue: arXiv cs.NI 2016
Year: 2016

Recent trends in targeted cyber-attacks has increased the interest of research in the field of cyber security. Such attacks have massive disruptive effects on rganizations, enterprises and governments. Cyber kill chain is a model to describe cyber-attacks so as to develop incident response and analysis capabilities. Cyber kill chain in simple terms is an attack chain, the path that an intruder takes to penetrate information systems over time to execute an attack on the target. This paper broadly categories the methodologies, techniques and tools involved in cyber-attacks. This paper intends to help a cyber security researcher to realize the options available to an attacker at every stage of a cyber-attack.
A Low-Complexity Transceiver Design in Sparse Multipath Massive MIMO Channels
Venue: arXiv cs.IT 2016
Year: 2016

In this letter, we develop a low-complexity transceiver design, referred to as semi-random beam pairing (SRBP), for sparse multipath massive MIMO channels. By exploring a sparse representation of the MIMO channel in the virtual angular domain, we generate a set of transmit-receive beam pairs in a semi-random way to support the simultaneous transmission of multiple data streams. These data streams can be easily separated at the receiver via a successive interference cancelation (SIC) technique, and the power allocation among them are optimized based on the classical waterfilling principle. The achieved degree of freedom (DoF) and capacity of the proposed approach are analyzed. Simulation results show that, compared to the conventional singular value decomposition (SVD)-based method, the proposed transceiver design can achieve near-optimal DoF and capacity with a significantly lower computational complexity.
Discovery of Latent Factors in High-dimensional Data Using Tensor Methods
Venue: arXiv cs.LG 2016
Year: 2016

Unsupervised learning aims at the discovery of hidden structure that drives the observations in the real world. It is essential for success in modern machine learning. Latent variable models are versatile in unsupervised learning and have applications in almost every domain. Training latent variable models is challenging due to the non-convexity of the likelihood objective. An alternative method is based on the spectral decomposition of low order moment tensors. This versatile framework is guaranteed to estimate the correct model consistently. My thesis spans both theoretical analysis of tensor decomposition framework and practical implementation of various applications. This thesis presents theoretical results on convergence to globally optimal solution of tensor decomposition using the stochastic gradient descent, despite non-convexity of the objective. This is the first work that gives global convergence guarantees for the stochastic gradient descent on non-convex functions with exponentially many local minima and saddle points. This thesis also presents large-scale deployment of spectral methods carried out on various platforms. Dimensionality reduction techniques such as random projection are incorporated for a highly parallel and scalable tensor decomposition algorithm. We obtain a gain in both accuracies and in running times by several orders of magnitude compared to the state-of-art variational methods. To solve real world problems, more advanced models and learning algorithms are proposed. This thesis discusses generalization of LDA model to mixed membership stochastic block model for learning user communities in social network, convolutional dictionary model for learning word-sequence embeddings, hierarchical tensor decomposition and latent tree structure model for learning disease hierarchy, and spatial point process mixture model for detecting cell types in neuroscience.
Towards Anthropo-inspired Computational Systems: the $P^3$ Model
Venue: arXiv cs.AI 2016
Year: 2016

This paper proposes a model which aim is providing a more coherent framework for agents design. We identify three closely related anthropo-centered domains working on separate functional levels. Abstracting from human physiology, psychology, and philosophy we create the $P^3$ model to be used as a multi-tier approach to deal with complex class of problems. The three layers identified in this model have been named PhysioComputing, MindComputing, and MetaComputing. Several instantiations of this model are finally presented related to different IT areas such as artificial intelligence, distributed computing, software and service engineering.